Railway systems, particularly in Germany, require high levels of automation to address legacy infrastructure challenges and increase train traffic safely. A key component of automation is robust long-range perception, essential for early hazard detection, such as obstacles at level crossings or pedestrians on tracks. Unlike automotive systems with braking distances of ~70 meters, trains require perception ranges exceeding 1 km. This paper presents an deep-learning-based approach for long-range 3D object detection tailored for autonomous trains. The method relies solely on monocular images, inspired by the Faraway-Frustum approach, and incorporates LiDAR data during training to improve depth estimation. The proposed pipeline consists of four key modules: (1) a modified YOLOv9 for 2.5D object detection, (2) a depth estimation network, and (3-4) dedicated short- and long-range 3D detection heads. Evaluations on the OSDaR23 dataset demonstrate the effectiveness of the approach in detecting objects up to 250 meters. Results highlight its potential for railway automation and outline areas for future improvement.
View on arXiv@article{sanchez2025_2504.18203,
title={ LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring },
author={ Raul David Dominguez Sanchez and Xavier Diaz Ortiz and Xingcheng Zhou and Max Peter Ronecker and Michael Karner and Daniel Watzenig and Alois Knoll },
journal={arXiv preprint arXiv:2504.18203},
year={ 2025 }
}